library(dplyr)
library(sf)
library(tidyverse)
library(leaflet)
library(sf)
library(leaflet.providers)
library(RColorBrewer)
library(tigris)
Dot Density Plot
education_url <- "https://github.com/dyerlab/ENVS-Lectures/raw/master/data/edu_attainment-shp.zip"
file <- tempfile(pattern = "edu", tmpdir = ".", fileext = ".zip")
file
[1] ".\\edu253c7a4044d8.zip"
download.file(education_url, destfile = file)
trying URL 'https://github.com/dyerlab/ENVS-Lectures/raw/master/data/edu_attainment-shp.zip'
Content type 'application/zip' length 285435 bytes (278 KB)
downloaded 278 KB
unzip(file, exdir = ".")
points <- st_read("edu_attainment-shp")
Reading layer `edu_attainment' from data source `C:\Users\Charis\Documents\Spring2021\GISinR\GISinR\ENVS-Lectures\edu_attainment-shp' using driver `ESRI Shapefile'
Simple feature collection with 15745 features and 1 field
geometry type: POINT
dimension: XY
bbox: xmin: -77.59891 ymin: 37.44779 xmax: -77.38568 ymax: 37.60228
geographic CRS: WGS 84
Convert education levels to factor - this allows us to use colorFactor() to create our color palette
points$education <- factor(points$education, levels = c("No HS diploma", "HS, no Bachelors", "Bachelors" , "Post-Bachelors" ))
Create color palette
factpal <- colorFactor(brewer.pal(n = 4, name = "RdBu"), points$education) #used to color categorical data
Create dot density plot
leaflet(points) %>%
addProviderTiles(providers$CartoDB.Positron)%>% #add provider tiles
addCircles(stroke = FALSE, fillOpacity = 1, #add points
color = ~factpal(education)) %>% #color based on educational attainment
addLegend(pal = factpal, values = ~education, title = "Education Attainment")%>% #add legend
addScaleBar(position = "bottomright") #add scale bar
Choropleth plot
load in the census tract data
tracts <- tracts("VA", "Richmond city") %>%
st_transform(4326)
Using FIPS code '51' for state 'VA'
Using FIPS code '760' for 'Richmond city'
Data Prep
edu_tracts <- tracts %>%
st_buffer(dist = 0) %>% # buffer by 0 to avoid error message
st_intersection(points) # intersect the two data sets
edu_tracts %>%
group_by(NAME, education) %>% # group by tract name and education level
tally() -> tally # tally() counts the number of rows for the groups, so output is one row for every attainment level for each tract (so if a tract has all 4 attainment levels, there are 4 rows of data for that tract)
tally
Simple feature collection with 246 features and 3 fields
geometry type: GEOMETRY
dimension: XY
bbox: xmin: -77.59891 ymin: 37.44779 xmax: -77.38568 ymax: 37.60228
geographic CRS: WGS 84
tally <- as.data.frame(tally) # converted to data.frame to be able to drop the geometry because the `pivot_wider()` function would not work the way I wanted it to when the geometry was present
tally <- tally %>%
select(NAME, education, n)%>% # do not want the geometry
pivot_wider(names_from = education, values_from = n) # pivot the table so that instead of having multiple rows per tract, you have one row per tract with a column for each attainment level
tally[is.na(tally)] <- 0 # define NA values as zero
Create attainment metric for choropleth
tally <- tally %>%
mutate(edu = (((`No HS diploma` * 1) + (`HS, no Bachelors` * 2) + (`Bachelors` * 3) + (`Post-Bachelors` * 4))/
(`No HS diploma` + `HS, no Bachelors` + `Bachelors` + `Post-Bachelors`)))
# I created a column, edu, and did a weighted average for each row. So 'No HS diploma' was weighted as 1, 'HS, no Bachelors' as 2, ect.
# I used this new column as my average education attainment value to color my polygons
tally %>%
select(NAME, edu) -> tally # no longer need the individual count data
Join the education data back with the tract data
tracts %>%
left_join(tally, by = "NAME") -> tracts
Create the palette
pal <- colorNumeric(
palette = "RdBu",
domain = tracts$edu) # This time I want a continuos palatte, so I used `colorNumeric()`
Make the choropleth map
leaflet(tracts) %>%
addProviderTiles(providers$CartoDB.Positron)%>% # add provider tiles
addPolygons( fillOpacity = 1, # add tract polygons
fillColor = ~pal(edu), smoothFactor = 0.2, # fill color based on the calculated 'edu' metric
color = "grey",
opacity = 1,
weight = 1) %>%
addLegend(pal = pal, values = ~edu, title = "Education Attainment") # add legend
I did not like this legend, as it’s unclear what the numbers mean. With some googling I found a solution
labels <-c("No HS diploma (1)", "HS, no Bachelors (2)",
"Bachelors (3)" , "Post-Bachelors (4)" ) # define the labels for the legend
leaflet(tracts) %>%
addProviderTiles(providers$CartoDB.Positron)%>% # add provider tiles
addPolygons( fillOpacity = 1, # add tract polygons
fillColor = ~pal(edu), smoothFactor = 0.2, # fill color based on the calculated 'edu' metric
color = "grey",
opacity = 1,
weight = 1) %>%
addLegend(pal = pal, values = ~edu,
opacity = 1,
title = "Education Attainment",
labFormat = function(type, cuts, p) { # Here's the trick
paste0(labels)
})
That looks better, I think
Combine the maps
Final map
---
title: "Leaflet Homework Key"
author: "Charis Deadwyler"
date: "3/15/2021"
output: html_notebook
---

```{r}
library(dplyr)
library(sf)
library(tidyverse)
library(leaflet)
library(sf)
library(leaflet.providers)
library(RColorBrewer)
library(tigris)
```

### Dot Density Plot
```{r}
education_url <- "https://github.com/dyerlab/ENVS-Lectures/raw/master/data/edu_attainment-shp.zip"

file <- tempfile(pattern = "edu", tmpdir = ".", fileext = ".zip")
file

download.file(education_url, destfile = file)
unzip(file, exdir = ".")


points <- st_read("edu_attainment-shp")
```

Convert education levels to factor - this allows us to use `colorFactor()` to create our color palette
```{r}
points$education <- factor(points$education, levels = c("No HS diploma", "HS, no Bachelors", "Bachelors" , "Post-Bachelors"  )) 
```

Create color palette
```{r}
factpal <- colorFactor(brewer.pal(n = 4, name = "RdBu"), points$education) #used to color categorical data
```


Create dot density plot
```{r}
leaflet(points) %>% 
  addProviderTiles(providers$CartoDB.Positron)%>% #add provider tiles
  addCircles(stroke = FALSE,  fillOpacity = 1, #add points
              color = ~factpal(education)) %>% #color based on educational attainment
  addLegend(pal = factpal, values = ~education, title = "Education Attainment")%>% #add legend
  addScaleBar(position = "bottomright") #add scale bar
```

### Choropleth plot

load in the census tract data
```{r}
tracts <- tracts("VA", "Richmond city") %>%
  st_transform(4326)
```

Data Prep
```{r message=FALSE, warning=FALSE}
edu_tracts <- tracts %>%
  st_buffer(dist = 0) %>% # buffer by 0 to avoid error message
  st_intersection(points) # intersect the two data sets
```

```{r message=FALSE, warning=FALSE}
edu_tracts %>%
  group_by(NAME, education) %>% # group by tract name and education level
  tally() -> tally # tally() counts the number of rows for the groups, so output is one row for every attainment level for each tract (so if a tract has all 4 attainment levels, there are 4 rows of data for that tract)

tally
```

```{r}
tally <- as.data.frame(tally) # converted to data.frame to be able to drop the geometry because the `pivot_wider()` function would not work the way I wanted it to when the geometry was present

tally <- tally %>%
  select(NAME, education, n)%>% # do not want the geometry 
  pivot_wider(names_from = education, values_from = n) # pivot the table so that instead of having multiple rows per tract, you have one row per tract with a column for each attainment level

tally[is.na(tally)] <- 0 # define NA values as zero
```

Create attainment metric for choropleth

```{r}
tally <- tally %>%
  mutate(edu = (((`No HS diploma` * 1) + (`HS, no Bachelors` * 2) + (`Bachelors` * 3) + (`Post-Bachelors` * 4))/
                  (`No HS diploma`  + `HS, no Bachelors` + `Bachelors` + `Post-Bachelors`)))

# I created a column, edu, and did a weighted average for each row. So 'No HS diploma' was weighted as 1, 'HS, no Bachelors' as 2, ect. 

# I used this new column as my average education attainment value to color my polygons
```

```{r}
tally %>%
  select(NAME, edu) -> tally # no longer need the individual count data
```

Join the education data back with the tract data

```{r}
tracts %>%
  left_join(tally, by = "NAME") -> tracts
```

Create the palette

```{r}
pal <- colorNumeric(
  palette = "RdBu",
  domain = tracts$edu) # This time I want a continuos palatte, so I used `colorNumeric()`
```

Make the choropleth map

```{r}
leaflet(tracts) %>%
  addProviderTiles(providers$CartoDB.Positron)%>% # add provider tiles
  addPolygons( fillOpacity = 1, # add tract polygons
               fillColor = ~pal(edu), smoothFactor = 0.2, # fill color based on the calculated 'edu' metric
               color = "grey", 
               opacity = 1,
               weight = 1) %>%
  addLegend(pal = pal, values = ~edu, title = "Education Attainment") # add legend
```

I did not like this legend, as it's unclear what the numbers mean. With some googling I found a solution

```{r}
labels <-c("No HS diploma (1)", "HS, no Bachelors (2)",
           "Bachelors (3)" , "Post-Bachelors (4)"  ) # define the labels for the legend
```

```{r}
leaflet(tracts) %>%
  addProviderTiles(providers$CartoDB.Positron)%>% # add provider tiles
  addPolygons( fillOpacity = 1, # add tract polygons
               fillColor = ~pal(edu), smoothFactor = 0.2, # fill color based on the calculated 'edu' metric
               color = "grey", 
               opacity = 1,
               weight = 1) %>%
  addLegend(pal = pal, values = ~edu, 
            opacity = 1,
            title = "Education Attainment",
            labFormat = function(type, cuts, p) {  # Here's the trick
              paste0(labels)
            })
```
That looks better, I think

### Combine the maps

```{r}

leaflet() %>% # I decided to define by data by layer because I have multiple data sets
  addProviderTiles(providers$CartoDB.Positron) %>% # Add provider tiles
  addPolygons(data = tracts, # I decided to plot the tracts on the dot density map
              color = "black",
              weight = 1,
              fillOpacity = 0,
              group = "Circle" # Need to groups, 1 for density plot and 1 for choropleth
              )  %>%
  addCircles(data = points,
             stroke = FALSE,  fillOpacity = 1, #plot the circles on top of the tracts
             color = ~factpal(education),
             group = "Circle" # Define group
             ) %>%
  addLegend(data = points,
            pal = factpal, # Add legend for dot density plot
            values = ~education,
            opacity = 1,
            title = "Educational Attainment",
            group = "Circle" # Define group for legend
            ) %>%
  groupOptions("Circle", zoomLevels = 1:12 # Define the zoom levels for the dot density plot (dots will only by visible between zoom levels 1 and 12)
               ) %>%
  addPolygons( data = tracts, # First layer of the choropleth map, tract polygons
               fillOpacity = .75,
               fillColor = ~pal(edu), smoothFactor = 0.2, # Color defined by attainment metric
               color = "grey",
               opacity = 1,
               weight = 1,
               group = "Choro" # Called my second group "Choro"
               ) %>%
  addLegend(data = tracts,
            pal = pal, # Add legend for choropleth plot
            values = ~edu,
            opacity = .75,
            title = "Educational Attainment",
            labFormat = function(type, cuts, p) {  # Here's the trick again
              paste0(labels)
            },
            group = "Choro" # Add to choropleth group
            ) %>%
 groupOptions("Choro", zoomLevels = 13:20  # Define levels for the choropleth map (choropleth polygons will only be visble between zoom levls 13 and 20)
              ) %>%
  addScaleBar(position = "bottomright") # Add scale bar. Scale bar does not need a group because I want it visible at all zoom levels
 


```

Final map


























